Low Cost IMU \ GPS Integration Using Kalman Filtering for Land Vehicle Navigation Application

Authors: Othman Maklouf, Abdurazag Ghila, Ahmed Abdulla, Ameer Yousef

Abstract:

Land vehicle navigation system technology is a subject of great interest today. Global Positioning System (GPS) is a common choice for positioning in such systems. However, GPS alone is incapable of providing continuous and reliable positioning, because of its inherent dependency on external electromagnetic signals. Inertial Navigation is the implementation of inertial sensors to determine the position and orientation of a vehicle. As such, inertial navigation has unbounded error growth since the error accumulates at each step. Thus in order to contain these errors some form of external aiding is required. The availability of low cost Micro-Electro-Mechanical-System (MEMS) inertial sensors is now making it feasible to develop Inertial Navigation System (INS) using an inertial measurement unit (IMU), in conjunction with GPS to fulfill the demands of such systems. Typically IMU’s are very expensive systems; however this INS will use “low cost” components. Unfortunately with low cost also comes low performance and is the main reason for the inclusion of GPS and Kalman filtering into the system. The aim of this paper is to develop a GPS/MEMS INS integrated system, which is able to provide a navigation solution with accuracy levels appropriate for land vehicle navigation. The primary piece of equipment used was a MEMS-based Crista IMU (from Cloud Cap Technology Inc.) and a Garmin GPS 18 PC (which is both a receiver and antenna). The integration of GPS with INS can be implemented using a Kalman filter in loosely coupled mode. In this integration mode the INS error states, together with any navigation state (position, velocity, and attitude) and other unknown parameters of interest, are estimated using GPS measurements. All important equations regarding navigation are presented along with discussion.

Keywords: GPS, IMU, Kalman Filter.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1335920

References:

[1] Mohander S. Grewal, Lawrance R. Weill, Angus P. Anderws "Global positioning system inertial navigation system and integration,” Copyright © 2001 A JohnWiley &Sons, Inc.
[2] Crista- Interface/Operation Document, 2004. Crista Inertial Measurement Unit (IMU) Interface / Operation Document. A Cloud Cap Technology Inc.
[3] Greg Welch, An Introduction to the Kalman Filter. Gary Bishop University: University of North Carolina at Chapel Hill Department of Computer Science Chapel Hill, NC 27599-3175.
[4] Eun-Hwan Shin, "Estimation Techniques for Low –Cost Inertial Navigation” Ph.D. Thesis, University of Calgary, May 2005.
[5] Saurabh Godha., "Performance Evaluation of Low Cost MEMS-Based IMU Integrated With GPS for Land Vehicle Navigation Application” Masters Thesis, the University of Calgary, Department of Geomatics Engineering, Calgary. FEBRUARY, 2006.
[6] El-Sheimy. "Inertial techniques and INS/DGPS Integration”. ENGO 623- Lecture Notes, the University of Calgary, Department of Geomatics Engineering, Calgary (2004).
[7] Walchko, K. and Mason, P., "Inertial Navigation,” 2002 FCRAR, FIT, Miami, 2002.
[8] 8. Sukkarieh, S., "Low Cost, High Integrity, Aided Inertial Navigation Systems for Autonomous Land Vehicles,” Ph.D. Thesis, University of Sydney, March 2000.
[9] Walchko, K., "Low Cost Inertial Navigation,” Masters’ Thesis, University of Florida, August 2002.
[10] GPS 18 Technical Specifications (190-00307-00). http://www.gpsinformation.net/waasgps.htm.